<title>High-speed sensor-based systems for mobile robotics</title>

Abstract

The success of machine vision systems in solving real-world problems will depend on how well they can balance the conflicting requirements of high accuracy, flexibility to operate under a wide range of environmental conditions, fast response time, and size constraints. Most machine vision systems developed in the past have sacrificed one or more of these factors in favor of the others. In this paper, we discuss our experience in developing high speed machine vision and world modeling systems for mobile robotics applications. A pipeline binocular stereo range detection system developed in our laboratory matches 256 x 256 pixel stereo image pairs in one second and generates 2- D and 3-D obstacle maps in near real-time. These obstacle maps then get integrated into pixel- and voxel-based dynamic world models. Using data provided by stereo cameras mounted on top of an indoor mobile robot, these systems have the capability to create very realistic models of the environment. An autonomous navigation system uses these environment models to successfully navigate a mobile robot in an indoor environment cluttered with dynamic and static obstacles.